Businesses, organizations, and various other types of entities (each hereinafter an “enterprise”) do not make business decisions just based on one factor, such as interest rate or revenues. Instead, enterprises generally rely on a total view when making business decisions such as, for example, how much to invest in research and development or whether to expand into a new market. Consequently, enterprises generally value the ability to integrate information from multiple perspectives for decision-making. Enterprises are prolific consumers of information technology and operate in an information ecosystem that has been experiencing an exponential growth in data volume over time. “Big data”—the storage and analysis of massive and/or complex data sets, has emerged as a solution to deal with this vast amount of data.
Existing “big data” technologies attempt to converge massive amounts of data. Although the value of data that can be linked with other data is tremendous, such strategy is neither physically nor temporally feasible. Besides the scale, big data can include data from different sources, and each source can produce data of different types or formats. Many existing technologies, even if they can handle the scale, have difficulty handling data that exists in different formats (e.g., structured, semi-structured, unstructured), hosted across different infrastructures (e.g., cloud, mainframe, custom-hardware), in different databases, and/or using different schemas.
Existing “big data” technologies also have difficulties managing knowledge at the enterprise level. For example, the information technology environment of an enterprise can include multiple computer systems that may be functionally and/or geographically diverse. Each such system can generally manage its data, information and knowledge in a specific way to achieve its goals. Moreover, at the system level, information technology systems are typically optimized for data storage and retrieval, and in some cases, specific types of analysis (e.g., report generation). In such a multi-system architecture with system-level optimizations, integrating the underlying systems is generally difficult and may require data integration or schema manipulation. However, such techniques are generally complex and time consuming.
As a consequence of these and other limitations of existing “big data” technologies, enterprises have limited ability to discover valuable information that can give them an edge in the competitive environment.